Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Phi 3 Mini 3.8B needs ~10.3 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~53 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
60.8 tok/s
TTFT
3184 ms
Safe context
21K
Memory
10.3 GB / 12.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 53.2 tok/s | 1985 ms | 21K |
| Coding | B | Tight fit | 53.2 tok/s | 3639 ms | 21K |
| Agentic Coding | F | Too heavy | 60.8 tok/s | 4632 ms | 21K |
| Reasoning | B | Tight fit | 60.8 tok/s | 3763 ms | 21K |
| RAG | F | Too heavy | 60.8 tok/s | 5789 ms | 21K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B65 |
Q3_K_S | 3 | 1.9 GB | Low | B65 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Adds memory headroom for longer context windows and future model growth.
~$625 MSRP
Yes, RTX 4070 Super 12GB can run Phi 3 Mini 3.8B with a B grade (Tight fit). Expected decode speed: 53.2 tok/s.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4070 Super 12GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.
For coding workloads, Phi 3 Mini 3.8B on RTX 4070 Super 12GB receives a B grade with 53.2 tok/s and 21K context.
On RTX 4070 Super 12GB, Phi 3 Mini 3.8B can safely use up to 21K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3-mini-3.8b-on-rtx-4070-super-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.1 GB |
| Medium |
| B65 |
Q4_K_M | 4 | 2.3 GB | Medium | B65 |
Q5_K_M | 5 | 2.7 GB | High | B66 |
Q6_K | 6 | 3.1 GB | High | B66 |
Q8_0 | 8 | 4.1 GB | Very High | B68 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B69 |